Gaussian Processes for Machine Learning

C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning , the MIT Press, 2006,ISBN 2006 Massachusetts Institute of 2 RegressionSupervised Learning can be divided into regression and classification the outputs for classification are discrete class labels, regression isconcerned with the prediction of continuous quantities. For example, in a fi-nancial application, one may attempt to predict the price of a commodity asa function of interest rates, currency exchange rates, availability and this chapter we describe Gaussian process methods for regression problems;classification problems are discussed in chapter are several ways to interpret Gaussian process (GP) regression can think of a Gaussian process as defining a distribution over functions,and inference taking place directly in the space of functions, thefunction-spacetwo equivalent viewsview.

that can be changed by setting certain parameters and in section 2.3 we discuss how the properties change as these parameters are varied. The predictions from a GP model take the form of a full predictive distribution; in section 2.4 we discuss how to combine a loss …

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